# import Image
from IPython.display import display, Image, HTML, IFrame, YouTubeVideo
default_size = {'width': 1024, 'height': 600}
Image(filename="../utils/images/pydata_logo.png", width='300px')
IFrame('https://pydata.org/code-of-conduct/', **default_size)
IFrame('https://numfocus.org/sponsored-projects', **default_size)
Image('../utils/images/pydata-map.png', width=650, height=100)
#Image('../utils/images/pydata-map.png')
Image('../utils/images/pydata-uk-map.png', width=600, height=400)
Image('../utils/images/pydata-london-screenshot.png', width=600, height=400)
Image(filename="../utils/images/man.png", width=100)
IFrame('https://pythondeadlin.es/', **default_size)
Image('../utils/images/pydata-london-2025-web.png', **default_size)
Abstract: In data science experimentation is vital, the more we can experiment, the more we can learn. However quick iteration isn't sufficient, we also need to be able to easily promote these experiments to production to deliver value. This requires all the stability and reliability of any production system.
John will disucssed building platforms that treat iteration as a first class consideration, the role of open source libraries, and balancing trade-offs.
Abstract:
With context window on the rise, there's been an increase in the number of tools who depend on entire codebases. In this talk, Saurav will discuss some alternative techniques one could use to provide quality relevant context to build better AI Code Agents with some example projects.